Importance of the field: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of ...

DNA methyltransferases (DNMTs) are important enzymes involved in epigenetic control of gene expression and represent valuable targets in cancer chemotherapy. A number of nucleoside DNMT inhibitors (DNMTi) have been studied in cancer, including in cancer stem cells, and two of them (azacytidine and decitabine) have been approved for treatment of myelodysplastic syndromes. However, only a few non-nucleoside DNMTi have been identified so far, and even fewer have been validated in cancer. Through a process of hit-to-lead optimization, we report here the discovery of compound 5 as a potent non-nucleoside DNMTi that is also selective toward other AdoMet-dependent protein methyltransferases. Compound 5 was potent at single-digit micromolar concentrations against a panel of cancer cells and was less toxic in peripheral blood mononuclear cells than two other compounds tested. In mouse medulloblastoma stem cells, 5 inhibited cell growth, whereas related compound 2 showed high cell differentiation. To the best of our knowledge, 2 and 5 are the first non-nucleoside DNMTi tested in a cancer stem cell line.

An exhaustive description of the molecular recognition mechanism between a ligand and its biological target is of great value because it provides the opportunity for an exogenous control of the related process. Very often this aim can be pursued using high resolution structures of the complex in combination with inexpensive computational protocols such as docking algorithms. Unfortunately, in many other cases a number of factors, like protein flexibility or solvent effects, increase the degree of complexity of ligand/protein interaction and these standard techniques are no longer sufficient to describe the binding event. We have experienced and tested these limits in the present study in which we have developed and revealed the mechanism of binding of a new series of potent inhibitors of Adenosine Deaminase. We have first performed a large number of docking calculations, which unfortunately failed to yield reliable results due to the dynamical character of the enzyme and the complex role of the solvent. Thus, we have stepped up the computational strategy using a protocol based on metadynamics. Our approach has allowed dealing with protein motion and solvation during ligand binding and finally identifying the lowest energy binding modes of the most potent compound of the series, 4-decyl-pyrazolo[1,5- a ]pyrimidin-7-one.